PL-VIO: Tightly-Coupled Monocular Visual–Inertial Odometry Using Point and Line Features

	PL-VIO: Tightly-Coupled Monocular Visual–Inertial Odometry Using Point and Line Features
	He YiJia(贺一家)1,2 ; Zhao Ji(赵季)3; Guo Yue(郭跃)1,2 ; He WenHao(何文浩)1 ; Yuan Kui(原魁)1
发表期刊	Sensors
	2018-04
卷号	18 期号:4 页码:1159
其他摘要	To address the problem of estimating camera trajectory and to build a structural three-dimensional (3D) map based on inertial measurements and visual observations, this paper proposes point–line visual–inertial odometry (PL-VIO), a tightly-coupled monocular visual–inertial odometry system exploiting both point and line features. Compared with point features, lines provide significantly more geometrical structure information on the environment. To obtain both computation simplicity and representational compactness of a 3D spatial line, Plücker coordinates and orthonormal representation for the line are employed. To tightly and efficiently fuse the information from inertial measurement units (IMUs) and visual sensors, we optimize the states by minimizing a cost function which combines the pre-integrated IMU error term together with the point and line re-projection error terms in a sliding window optimization framework. The experiments evaluated on public datasets demonstrate that the PL-VIO method that combines point and line features outperforms several state-of-the-art VIO systems which use point features only.
关键词	Sensor Fusion Visual–inertial Odometry Tightly-coupled Point And Line Features
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/21064
专题	智能制造技术与系统研究中心_智能机器人
作者单位	1.中国科学院自动化研究所 2.中国科学院大学 3.上海阅面科技有限公司
第一作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	He YiJia(贺一家),Zhao Ji(赵季),Guo Yue(郭跃),等. PL-VIO: Tightly-Coupled Monocular Visual–Inertial Odometry Using Point and Line Features[J]. Sensors,2018,18(4):1159.
APA	He YiJia,Zhao Ji,Guo Yue,He WenHao,&Yuan Kui.(2018).PL-VIO: Tightly-Coupled Monocular Visual–Inertial Odometry Using Point and Line Features.Sensors,18(4),1159.
MLA	He YiJia,et al."PL-VIO: Tightly-Coupled Monocular Visual–Inertial Odometry Using Point and Line Features".Sensors 18.4(2018):1159.

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